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This paper recalls the practical calculation of Learning Entropy (LE) for novelty detection, extends it for various gradient techniques and discusses its use for multivariate dynamical systems with ability of distinguishing between data perturbations or system-function perturbations. LG has been recently introduced for novelty detection in time series via(More)
During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several(More)
This paper presents a modification of reference-model adaptive control with a layered network of higher-order neural units (HONUs) as adaptive state-feedback controllers. The degree of freedom of such neural controller is deemed here as the number of applied HONUs of a customizable polynomial order and as of their individually customizable input vectors.(More)
This paper introduces a neural network approach to hoist deceleration control of industrial hoist mechanisms, with particular focus to crane applications. The necessity for investigation in this field arises from the increasing demands in terms of safety within in the industry. Should the industrial hoist feature too high deceleration this can lead to(More)
The paper presents a study of an adaptive approach to lateral skew control for an experimental railway stand. The preliminary experiments with the real experimental railway stand and simulations with its 3-D mechanical model, indicates difficulties of model-based control of the device. Thus, use of neural networks for identification and control of lateral(More)
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